System and method for managing routing of customer calls to agents

ABSTRACT

A call management system of a call center retrieves customer demographic data associated with a customer identifier for an inbound caller, i.e., customer. A predictive model including a logistic regression model and tree based model determines a value prediction signal for the identified customer. Based on the value prediction signal determined, the predictive model classifies the identified customer into a first value group or a second value group. The call management system routes a customer classified in the first value group to a first call queue for connection to one of a first pool of call center agents who are authorized to present an offer to purchase a product, and routes a customer classified in the second value group to a second call queue for connection to one of a second pool of call center agents who are not authorized to present the offer to purchase the product.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/283,378, entitled “SYSTEM AND METHOD FOR MANAGING ROUTING OF CUSTOMERCALLS TO AGENTS,” filed Feb. 22, 2019, which is a continuation of U.S.patent application Ser. No. 16/110,872, entitled “SYSTEM AND METHOD FORMANAGING ROUTING OF CUSTOMER CALLS TO AGENTS,” filed Aug. 23, 2018,which claims the benefit of U.S. Provisional App. No. 62/551,690, filedAug. 29, 2017, claims the benefit of U.S. Provisional App. No.62/648,325, filed Mar. 26, 2018, claims the benefit of U.S. ProvisionalApp. No. 62/648,330, filed Mar. 26, 2018, and claims the benefit of U.S.Provisional App. No. 62/687,130, filed Jun. 19, 2018, all of which areincorporated by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates generally to customer contact centers andtheir operation, and more particularly to a system and method formanaging routing of customer calls to agents.

BACKGROUND

Customer contact centers provide an important interface forcustomers/partners of an organization to contact the organization. Thecontact can be for a request for a product or service, for troublereporting, service request, etc. The contact mechanism in a conventionalcall center is via a telephone, but it could be via a number of otherelectronic channels, including e-mail, online chat, etc.

The contact center consists of a number of human agents, each assignedto a telecommunication device, such as a phone or a computer forconducting email or Internet chat sessions, that is connected to acentral switch. Using these devices, the agents generally provide sales,customer service, or technical support to the customers or prospectivecustomers of a contact center, or of a contact center's clients.Conventionally, a contact center operation includes a switch system thatconnects callers to agents. In an inbound contact center, these switchesroute inbound callers to a particular agent in a contact center, or, ifmultiple contact centers are deployed, to a particular contact centerfor further routing. When a call is received at a contact center (whichcan be physically distributed, e.g., the agents may or may not be in asingle physical location), if a call is not answered immediately, theswitch will typically place the caller on hold and then route the callerto the next agent that becomes available. This is sometimes referred toas placing the caller is in a call queue. In conventional methods ofrouting inbound callers to agents, high business value calls can besubjected to a long wait while the low business value calls are oftenanswered more promptly, possibly causing dissatisfaction on the part ofthe high business value caller.

There is a need for a system and method for identifying high businessvalue inbound callers at a call center during a time period in whichinbound callers are awaiting connection to an agent. Additionally, thereis a need to improve traditional methods of routing callers, such as“round-robin” caller routing, to improve allocation of limited callcenter resources to high business value inbound callers.

SUMMARY

Embodiments described herein can automatically route an inbound callfrom a customer to one of a plurality of queues (e.g., two queues) basedon predicted value of the inbound telephone call. Upon identifying thecustomer, the process retrieves customer demographic data associatedwith a customer identifier for the identified customer. A predictivemodel determines a value prediction signal for the identified customer.Based on the value prediction signal determined, the predictive modelclassifies the identified customer into one of a first value group and asecond value group. In the event the predictive model classifies theidentified customer into the first value group, the call managementsystem routes the identified customer to a first call queue forconnection to one of a first pool of call center agents who areauthorized to present the offer to purchase the product. In the eventthe predictive model classifies the identified customer into the secondvalue group, the call management system routes the identified customerto a second call queue for connection to one of a second pool of callcenter agents who are not authorized to present the offer to purchasethe product.

The predictive model can include a logistic regression model and a treebased model. In an embodiment, the predictive model determines the valueprediction signal in real time by applying a logistic regression modelin conjunction with a tree based model to the retrieved customerdemographic data. In an embodiment, the logistic regression modelemploys l₁ regularization. In an embodiment, the logistic regressionmodel employs l₂ regularization. In an embodiment, the tree based modelis a random forests ensemble learning method for classification.

The value prediction signal can include one or more of a first signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase a product, a second signal representative of alikelihood that the identified customer will lapse in payments for apurchased product, and a third signal representative of a likelihoodthat the identified customer will accept an offer to purchase theproduct and will not lapse in payments for the purchased product. Invarious embodiments, the a value prediction signal is a buy-only signal,a lapse-only signal, a buy-don't-lapse signal, or combination of thesesignals.

The customer identifier can include two or more of name of theidentified customer, address of the identified customer, and zip code ofthe identified customer. In an embodiment, the customer managementsystem obtains a customer identifier for the received customer call fromcaller information associated with the inbound calls. In an embodiment,the customer management system obtains a customer identifier for thereceived customer call via an automated telegreeter of a Voice ResponseUnit (“VRU”) system. In an embodiment, the customer management systemobtains a customer identifier for the received customer call from athird party directory service.

In one embodiment, a processor-based method comprises executing, by aprocessor, a predictive machine-learning model configured to determine,for each lead profile of a plurality of lead records stored in aninternal database of a contact center, a value prediction signal byinputting customer demographic data, payment data, marketing costs data,and lapse data into a logistic regression model operating in conjunctionwith a tree based model, the predictive machine-learning modeloutputting a first subset of the plurality of lead records into a firstvalue group and a second subset of the plurality of lead records into asecond value group, wherein the value prediction signal comprises one ormore of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product, and wherein the predictive machine-learning model iscontinually trained using customer demographic data, updated paymentdata, updated marketing costs data, and updated lapse data; and running,by the processor, the predictive machine-learning model upon receiving acustomer call from an identified customer at an inbound call receivingdevice of the contact center to: retrieve the customer demographic datafor the identified customer; classify the identified customer into oneof the first value group and the second value group; and direct theinbound call receiving device: in the event the processor classifies theidentified customer into the first value group, to route the identifiedcustomer to a first call queue for connection to one of a first pool ofcall center agents; in the event the processor classifies the identifiedcustomer into the second value group, to route the identified customerto a second call queue for connection to one of a second pool of callcenter agents.

In another embodiment, a processor based method for managing customercalls within a call center comprises retrieving, by a processor,customer demographic data associated with a customer identifier for anidentified customer in a customer call; determining, by a predictivemodel executing on the processor, a value prediction signal comprisingone or more of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product; wherein the predictive model comprises a logisticregression model operating in conjunction with a tree based model;classifying, by the predictive model executing on the processor based onthe value prediction signal determined by the predictive model, theidentified customer into one of a first value group and a second valuegroup, wherein the first value group comprises customers having a firstset of modeled lifetime values, and the second value group comprisescustomers having a second set of modeled lifetime values, whereinmodeled lifetime values in the first set of modeled lifetime values arehigher than modeled lifetime values in the second set of modeledlifetime values; and in the event the classifying step classifies theidentified customer into the first value group, routing, by theprocessor, the identified customer to a first call queue for connectionto one of a first pool of call center agents; in the event theclassifying step classifies the identified customer into the secondvalue group, routing, by the processor, the identified customer to asecond call queue for connection to one of a second pool of call centeragents.

1. In a further embodiment, a system for managing customer calls withina call center, comprises an inbound telephone call receiving device forreceiving a customer call to the call center; non-transitorymachine-readable memory that stores historical information for leads ofthe call center comprising payment data, marketing costs data, and lapsedata; a predictive modeling module that stores a predictive model ofcustomer value, wherein the predictive model comprises a logisticregression model operating in conjunction with a tree based model; and aprocessor, configured to execute an inbound queue management module,wherein the processor in communication with the non-transitorymachine-readable memory and the predictive models module executes a setof instructions instructing the processor to: retrieve customerdemographic data associated with a customer identifier for an identifiedcustomer in the customer call received by the inbound telephone callreceiving device, wherein the customer identifier comprises two or moreof name of the identified customer, address of the identified customer,and zip code of the identified customer; retrieve from thenon-transitory machine readable memory the historical information forleads of the call center comprising payment data, marketing costs data,and lapse data; determine a value prediction signal for the identifiedcustomer via analysis by the predictive model of the customerdemographic data associated with the customer identifier for theidentified customer and via analysis of historical payment data andlapse data of the call center; wherein the value prediction signalcomprises one or more of a first signal representative of a likelihoodthat the identified customer will accept an offer to purchase a product,a second signal representative of a likelihood that the identifiedcustomer will lapse in payments for a purchased product, and a thirdsignal representative of a likelihood that the identified customer willaccept an offer to purchase the product and will not lapse in paymentsfor the purchased product; classify the identified customer into one ofa first value group and a second value group based on the valueprediction signal, wherein the first value group comprises customershaving a first set of modeled lifetime values, and the second valuegroup comprises customers having a second set of modeled lifetimevalues, wherein modeled lifetime values in the first set of modeledlifetime values are higher than modeled lifetime values in the secondset of modeled lifetime values; and direct the inbound telephone callreceiving device: in the event the inbound queue management moduleclassifies the identified customer into the first value group, to routethe identified customer to a first call queue for connection to one of afirst pool of call center agents; in the event the inbound queuemanagement module classifies the identified customer into the secondvalue group, to route the identified customer to a second call queue forconnection to one of a second pool of call center agents.

In another embodiment, a processor-based method comprises executing, bya processor, a predictive machine-learning model configured todetermine, for each lead profile of a plurality of lead records storedin an internal database of a contact center, a value prediction signalby inputting customer demographic data, payment data and lapse data intoa logistic regression model operating in conjunction with a tree basedmodel, the predictive machine-learning model outputting a first subsetof the plurality of lead records into a first value group and a secondsubset of the plurality of lead records into a second value group,wherein the value prediction signal comprises one or more of a firstsignal representative of a likelihood that the identified customer willaccept an offer to purchase a product, a second signal representative ofa likelihood that the identified customer will lapse in payments for apurchased product, and a third signal representative of a likelihoodthat the identified customer will accept an offer to purchase theproduct and will not lapse in payments for the purchased product, andwherein the predictive machine-learning model is continually trainedusing customer demographic data, updated payment data, and updated lapsedata; and running, by the processor, the predictive machine-learningmodel upon receiving a customer call from an identified customer at aninbound call receiving device of the contact center: to retrieve thecustomer demographic data for the identified customer; to classify theidentified customer into one of the first value group and the secondvalue group; and to direct the inbound call receiving device: in theevent the processor classifies the identified customer into the firstvalue group, to route the identified customer to a first call queue forconnection to one of a first pool of call center agents who areauthorized to present the offer to purchase the product; in the eventthe processor classifies the identified customer into the second valuegroup, to route the identified customer to a second call queue forconnection to one of a second pool of call center agents who are notauthorized to present the offer to purchase the product.

In another embodiment, a processor based method for managing customercalls within a call center comprises retrieving, by a processor,customer demographic data associated with a customer identifier for anidentified customer in a customer call; determining, by a predictivemodel executing on the processor, a value prediction signal comprisingone or more of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product; wherein the predictive model comprises a logisticregression model operating in conjunction with a tree based model;classifying, by the predictive model executing on the processor based onthe value prediction signal determined by the predictive model, theidentified customer into one of a first value group and a second valuegroup; and in the event the classifying step classifies the identifiedcustomer into the first value group, routing, by the processor, theidentified customer to a first call queue for connection to one of afirst pool of call center agents who are authorized to present the offerto purchase the product; in the event the classifying step classifiesthe identified customer into the second value group, routing, by theprocessor, the identified customer to a second call queue for connectionto one of a second pool of call center agents who are not authorized topresent the offer to purchase the product.

In yet another embodiment, a system for managing customer calls within acall center, comprises an inbound telephone call receiving device forreceiving a customer call to the call center; non-transitorymachine-readable memory that stores historical information about leads,customers, and marketing costs of the call center; a predictive modelingmodule that stores a predictive model of customer value, wherein thepredictive model comprises a logistic regression model operating inconjunction with a tree based model; and a processor, configured toexecute an inbound queue management module, wherein the processor incommunication with the non-transitory machine-readable memory and thepredictive models module executes a set of instructions instructing theprocessor to: retrieve external third-party customer demographic dataassociated with a customer identifier for an identified customer in thecustomer call received by the inbound telephone call receiving device,wherein the customer identifier comprises two or more of name of theidentified customer, address of the identified customer, and zip code ofthe identified customer; retrieve from the non-transitory machinereadable memory the historical information about leads, customers, andmarketing costs of the call center; determine a value prediction signalfor the identified customer via analysis by the predictive model of thethird-party customer demographic data associated with the customeridentifier for the identified customer and via analysis of thehistorical information about leads, customers, and marketing costs ofthe call center; wherein the value prediction signal comprises one ormore of a first signal representative of a likelihood that theidentified customer will accept an offer to purchase a product, a secondsignal representative of a likelihood that the identified customer willlapse in payments for a purchased product, and a third signalrepresentative of a likelihood that the identified customer will acceptan offer to purchase the product and will not lapse in payments for thepurchased product; classify the identified customer into one of a firstvalue group and a second value group based on the value predictionsignal; and direct the inbound telephone call receiving device: in theevent the inbound queue management module classifies the identifiedcustomer into the first value group, to route the identified customer toa first call queue for connection to one of a first pool of call centeragents who are authorized to present the offer to purchase the product;in the event the inbound queue management module classifies theidentified customer into the second value group, to route the identifiedcustomer to a second call queue for connection to one of a second poolof call center agents who are authorized to present the offer topurchase the product.

Other objects, features, and advantages of the present disclosure willbecome apparent with reference to the drawings and detailed descriptionof the illustrative embodiments that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting embodiments of the present disclosure are described by wayof example with reference to the accompanying figures which areschematic and are not intended to be drawn to scale. Unless indicated asrepresenting the background art, the figures represent aspects of thedisclosure.

FIG. 1 is a system architecture for a customer management system of aninbound contact center, in accordance with an embodiment.

FIG. 2 illustrates a method for routing a customer call to an agent inaccordance with an embodiment.

FIG. 3 illustrates a queue arrangement for routing prospective customersto agents of a call center, in accordance with an embodiment.

FIG. 4 is a graph of a receiver operator curve (ROC) for a valueprediction model, in accordance with an embodiment.

FIG. 5 is a graph of a receiver operator curve (ROC) for a valueprediction model, in accordance with an embodiment.

FIG. 6 is a graph of lift across deciles of model scores for a valueprediction model, in accordance with an embodiment.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which depict non-limiting, illustrativeembodiments of the present disclosure. Other embodiments may be utilizedand logical variations, e.g., structural and/or mechanical, may beimplemented without departing from the scope of the present disclosure.To avoid unnecessary detail, certain information, items, or detailsknown to those skilled in the art may be omitted from the following.

Contact routing at an inbound contact center can be structured innumerous ways. An individual employed by the contact center to interactwith callers is referred to in the present disclosure as an “agent.”Contact routing can be structured to connect callers to agents that havebeen idle for the longest period of time. In the case of an inboundcaller where only one agent may be available, that agent is generallyselected for the caller without further analysis. In another examplerouting an inbound call, if there are eight agents at a contact center,and seven are occupied with callers, the switch will generally route theinbound caller to the one agent that is available. If all eight agentsare occupied with contacts, the switch will typically put the caller onhold and then route the caller to the next agent that becomes available.More generally, the contact center will set up a queue of inboundcallers and preferentially route the longest-waiting callers to theagents that become available over time. A pattern of routing callers toeither the first available agent or the longest-waiting agent issometimes referred to as “round-robin” caller routing.

In general, when a caller is placed in a call queue, the caller's queueposition is dependent upon the receipt time of the call at the vendorlocation. No consideration is given to the identity of the caller or thepotential value of the call. While this is a democratic way to handleinbound calls, it may not be good for business. For instance, a largenumber of low business value calls may be in a queue when a highbusiness value call is received. As a result, the high business valuecall is subjected to a long wait while the low business value calls areanswered—with attendant dissatisfaction on the part of the high businessvalue caller. When call centers have an inadequate number of skilledagents to handle all callers, such as at times of peak call volume,challenges of effectively handling high-value callers can be especiallysevere. The method and system of the present disclosure are intended toalleviate these problems.

Methods and systems described herein can automatically route an inboundcall from a customer to one of a plurality of queues (e.g., two queues)based on predicted value of the inbound telephone call. Upon identifyingthe customer, the process retrieves customer demographic data associatedwith a customer identifier for the identified customer. A predictivemodel determines a value prediction signal for the identified customer.Based on the value prediction signal determined, the predictive modelclassifies the identified customer into one of a first value group and asecond value group. In the event the predictive model classifies theidentified customer into the first value group, the call managementsystem routes the identified customer to a first call queue forconnection to one of a first pool of call center agents who areauthorized to present the offer to purchase the product. In the eventthe predictive model classifies the identified customer into the secondvalue group, the call management system routes the identified customerto a second call queue for connection to one of a second pool of callcenter agents who are not authorized to present the offer to purchasethe product.

Methods and systems described herein can employ a pre-sale predictivemodel relating to offer for sale of one or more product offered orsupplied by a sponsoring organization of an inbound contact center. Invarious embodiments, the product(s) offered or supplied by a sponsoringorganization require payments by the customer for a period followingclosing the sale, such as premiums to maintain in force an insurancepolicy or other financial product, or installment plans for productpurchase. In various embodiments, the pre-sale prediction modelincorporates information on a minimum period of time of customerpayments required to achieve a beneficial transaction for the sponsoringorganization, and uses this information in determining conditions for“lapse.” The presale predictive model forecasts customer behavior toimprove the probability of closing a sale of an offered product to aninbound customer, and to reduce the probability that the customer willlapse in payment for the purchased product.

The pre-sale predictive model can classify inbound callers into two, ormore, value groups. In an embodiment, two value groups are modeled tomodel higher predicted value and lower predicted value, respectively, tothe sponsoring organization. In various embodiments, this classificationgoverns value-based routing of inbound telephone calls for response byagents, to allocate limited resources of the inbound contact center.

The inbound contact center is sometimes called simply a contact centeror a call center. The individuals that interact with the contact centerusing a telecommunication device are referred to herein as callers, andalternatively are referred to as inbound callers, customers, prospectivecustomers, or leads. In the present disclosure, a “customer” may be anexisting customer or a prospective customer, and a prospective customeris alternatively called a lead. An individual employed by the contactcenter to interact with callers is referred to herein as an agent.

A pre-sale prediction model can incorporate information on a minimumperiod of time of customer payments required to achieve a beneficialtransaction for the sponsoring organization. Failure of a customer tomake payments over at least this minimum time period is sometimesreferred to herein as “lapse.” In an embodiment, pre-sale predictivemodels of the present disclosure incorporate a pre-determined period oftime of payments following the sale of the product to define lapse. Incertain embodiments, a sale of an insurance policy or other financialproduct requires only that the prospect complete an application topurchase the policy, sometimes called guaranteed acceptance. Whenselling via guaranteed acceptance, lapse rates for sold policies tend tobe higher.

A key metric for value-based classification of a customer who haspurchased a product is called a “lifetime value” of the product sale tothat customer. In various embodiment, lifetime value includes the sum ofall associated costs over product lifetime, netted against revenue forthe product sale. The lifetime value for the product (insurance policy)sold to that customer is the net value of all premiums paid, over thesum of all such associated costs during that policy life.

In an embodiment involving sale of an insurance policy, associated costsover product lifetime include various sales acquisition costs, includingmarketing costs distributed across inbound calls, cost of operating theinbound contact center distributed across inbound calls, and commissionat the time of sale. In this example, additional associated costsinclude cost of providing the insurance policy, and claims or deathbenefit. In various embodiments, total costs for a customer are modeledbased on the customer's age, gender, policy face amount, and whether thepolicy is lapsed, and by applying formulas based on amortization oftotal marketing costs and operations costs. In an embodiment involvingsale of an insurance policy, total revenue for a customer is modeledbased on the customer's age, gender, policy face amount, and whether thepolicy is lapsed (if so, when). The model calculates expected totalpremium payments based on age and gender via lookup of mortalitystatistics.

Methods and systems described herein can identify lapse (e.g., for agiven product or class of products) with a pre-determined period of timefollowing sale of the product, and define lapse as failure of thecustomer to make payments for the product over at least this period oftime. In various embodiments, this predetermined period of time is basedupon modeling a minimum period of time for achieving a positive lifetimevalue for the product sale. This model compare total payments receivedwith associated costs over different product lifetimes to determine thepredetermined period. In one embodiment, product lifetime represents aperiod of time over in which the customer has continued to make purchasepayments for the product, such as premiums or installment payments. Inanother embodiment, lifetime value is measured during the full term orlife of an insurance policy or other financial instrument until allclaims and death benefits have been paid, even if all premiums or othercustomer payments had been paid prior to this time.

FIG. 1 shows a system architecture for a customer management system 100,according to an illustrative embodiment. In the present disclosure, thecustomer management system 100 is sometimes called an inbound callcenter or inbound contact center, referring to its primary function ofreceiving inbound customer calls. Customer management system 100includes an inbound queue management system 102, also called an inboundcall management system. Inbound queue management system 102 managesassignment of inbound telephone calls for response by agents to multiplequeues (e.g., two queues) based on predicted value of the inboundtelephone call. Inbound queue management system 102 includes ananalytical engine 104 containing a call evaluation sub-module 108, and apredictive modeling module 110 including a regression model 114 and atree based model 118.

Inbound call management system 102 is interfaced with one or moreinternal databases 120 of the inbound contact center, such as callhistory database 124 and account information database 128. In anembodiment, analytical engine 104 interacts with external services,applications, and databases, such as third party databases 130, throughone or more application programmable interfaces, an RSS feed, or someother structured format, via communication network 135. In theembodiment of FIG. 1, inbound queue management system 102 retrieves datafrom one or more third party databases 130, including a consumerdemographic database 132 and a directory service database 134.

Predictive modeling module 110 builds one or more models that modelbehaviors of customers such as likelihood that a caller will purchase aproduct offered by the call center, and likelihood that the caller willlapse in payments for a purchased product. The predictive modelingmodule analyzes each inbound customer call using data associated with acustomer identifier for the inbound caller. This customer identifier maybe obtained from various sources by the call evaluation sub-module 108.Input data used in predictive modeling includes data retrieved from oneor more of internal databases 120, and third party databases 130. Thisinput data also may include data derived from the retrieved data thathas been transformed by analytical engine 104 in order to facilitatepredictive modeling, as described herein.

Databases 120 are organized collections of data, stored innon-transitory machine-readable storage. In an embodiment, the databasesmay execute or may be managed by database management systems (DBMS),which may be computer software applications that interact with users,other applications, and the database itself, to capture (e.g., storedata, update data) and analyze data (e.g., query data, execute dataanalysis algorithms). In some cases, the DBMS may execute or facilitatethe definition, creation, querying, updating, and/or administration ofdatabases. The databases may conform to a well-known structuralrepresentational model, such as relational databases, object-orienteddatabases, and network databases. Database management systems mayinclude MySQL, PostgreSQL, SQLite, Microsoft SQL Server, MicrosoftAccess, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base, FileMakerPro.

Analytical engine 104 can be executed by a server, one or more servercomputers, authorized client computing devices, smartphones, desktopcomputers, laptop computers, tablet computers, PDAs and other types ofprocessor-controlled devices that receive, process, and/or transmitdigital data. Analytical engine 104 can be implemented using asingle-processor system including one processor, or a multi-processorsystem including any number of suitable processors that may be employedto provide for parallel and/or sequential execution of one or moreportions of the techniques described herein. Analytical engine 104performs these operations as a result of central processing unitexecuting software instructions contained within a computer-readablemedium, such as within memory. In one embodiment, the softwareinstructions of the system are read into memory associated with theanalytical engine 104 from another memory location, such as from storagedevice, or from another computing device via communication interface. Inthis embodiment, the software instructions contained within memoryinstruct the analytical engine 104 to perform processes described below.Alternatively, hardwired circuitry may be used in place of or incombination with software instructions to implement the processesdescribed herein. Thus, implementations described herein are not limitedto any specific combinations of hardware circuitry and software.

Predictive modeling module 110 generates a value prediction signalrepresentative of one or more of the following customer behaviors: (a)likelihood that the customer will accept an offer to purchase a product,(b) likelihood that the customer will lapse in payments for a purchasedproduct, and (c) likelihood that the customer will accept an offer topurchase the product and will not lapse in payments for the purchasedproduct. In certain embodiments, the predictive modeling module canpredict more than one of these customer behaviors. For example, thepredictive model may first determine the customer behavior (a)likelihood that the customer will accept an offer to purchase a product,followed by determining the customer behavior (b) likelihood that thecustomer will lapse in payments for a purchased product, in order todetermine a value prediction signal. Based on this value predictionsignal, the analytical module, in conjunction with the predictivemodeling module, classifies each customer call into one of two, or more,value groups. Depending on the value group determined for each customercall, analytical engine 104 directs routing of the customer call to oneof two or more answering queues 150 to await connection to an agent ofthe call center. In FIG. 1, two call queues 154 and 158 are shown.Value-based classification of inbound calls by inbound call managementsystem 102 represents a significant improvement over traditional methodsof routing callers, such as “round-robin” caller routing.

Inbound call management system 102 interfaces with an inbound telephonecall receiving system 140. In customer management system 100, inboundcall management system 102 and call receiving system 140 may beintegrated in a single computing platform. Alternatively these systemsmay be based on separate computing platforms. In certain embodiments,the computing platform(s) are interfaced with computer-telephoneintegration (“CTI”) middleware. In an embodiment, inbound telephone callreceiving system 140 includes a telephony device that accepts inboundtelephone calls through a telephony interface 141, such as conventionalTi or fiber interfaces. Inbound telephone call receiving system 140accepts inbound telephone calls through interface 141 and obtains callerinformation associated with the inbound calls, such as Automatic NumberIdentification (“ANI”) and Dialed Number Identification Service (“DNIS”)information 145. ANI is a signaling system feature in which a series ofdigits, either analog or digital, are included in the call identifyingthe source telephone number of the calling device. DNIS is a telephonefunction that sends the dialed telephone number to an answering service.The DNIS need not be a telephone number associated with any physicallocation.

Inbound telephone call receiving system 140 may include an AutomaticCall Distributor (“ACD”) system 142; a Voice Response Unit (“VRU”)system 144; a private branch exchange (“PBX”) switch 146; a Voice overInternet Protocol (“VOIP”) server 148; or any combination of suchdevices. In an embodiment, intrasite telephony access within the callcenter may be managed by a private branch exchange (PBX) switch 146. Inan embodiment, PBX switch 146 operates in coordination with ACD 142 todistribute inbound calls customer service stations locally networkedcall center agents, such agents in first pool 160 and second pool 170.In further embodiments, inbound inquiries may include e-mail or instantmessages that provide inquiry information based on login ID, e-mailaddress, IP or instant message address. In such an embodiment, the callcenter can gather additional information by an automated e-mail orinstant message survey response, which can be used to request varioustypes of customer identifier data.

An identified customer can be an inbound caller for which the customermanagement system 100 has obtained reliable identifying data. This datais used by inbound queue management system 102 to retrieve or identifydata associated with that customer. In an embodiment, an identifiedcustomer is a customer for which the system 100 has reliably identifiedat least two of name, address, and zip code. In an embodiment, VoiceResponse Unit (“VRU”) system 144 collects customer identifier data, suchas name, address, and zip code, through automated interaction with thecustomer. In the present disclosure, this VRU data collection issometimes called a telegreeter. For instance, VRU 144 may query aninbound caller to collect customer identifier information when ANI isnot operative, e.g., when caller-ID is blocked. In an embodiment,inbound call management system 102 communicates with a third partydirectory service 134. Directory service 134 can provide additionalcaller identification information, such as name and address information,for inbound callers that are initially identified only by a telephonenumber. For an inbound caller that is a new lead of the enterprise, thisadditional caller identification information can be stored in AccountInfo database 128 as profile data for that lead.

Inbound telephone calls received through interface 141 are distributedto answering queues 150 for response by agents operating telephonydevices. In the embodiment of FIG. 1, answering queues 150 include afirst call queue 154 for response by one of the agents in a first pool160 of call center agents, and a second call queue 158 for response byone of the agents in a second pool of agents 170. Although FIG. 1depicts an embodiment of the present invention that orders inboundtelephone calls, alternative embodiments apply inbound queue managementto schedule other types of inbound inquiries, such as e-mail or instantmessage inquiries. In an embodiment, call center agents in agent pools160 and 170 are groups of customer service representatives or agentsdeployed at workstations or agent devices communicatively coupled tocall management system 100.

The agents are associated with a sponsoring organization that sells orsupplies products with the assistance of the call center. In anembodiment, the organization generates sales of one or more productthrough advertisements that give a phone number to prospectivecustomers, and the prospective customers call into the call center usingthis phone number. In various embodiments, the agents in first pool 160are authorized to offer an advertised product to a prospective customer(inbound caller), while the agents in second pool 170 are not authorizedto offer the advertised product. In the present disclosure, for an agentto be authorized to offer a product to a prospective customer or leadmeans that the agent is authorized to pursues a sale of the product tothe lead.

A sponsoring organization for customer management system 100 is aninsurance company or other financial services company, and the agentsmay include insurance agents. In some cases, an insurance agent may beassociated with only a single insurance provider (sometimes referred toas a “captive” insurance agent). In other cases, an “independent”insurance agent may be associated with several different insuranceproviders. In an embodiment, the agents in the first pool 160 arelicensed to sell insurance. In some cases, the producers may be licensedto sell different types of insurance products, might have differentareas of expertise, needs, etc. In some embodiments, agents in the firstpool 160 are selected for performance metrics related to sales. Agentsales performance may be measured by aggregate sales productivitymetrics, as well as distributed performance metrics such as salesmetrics by product types, etc.

While the agents in second pool 170 are not authorized to offer theproduct(s) to the inbound caller (prospective customer, or lead), theseagents perform an important role in lead nurturing. Forwarding aninbound inquiry to a live agent with little or no wait time, sometimesreferred to herein as a “warm transfer,” has been observed tosignificantly increase probability of a successful sale to that customerin a later interaction. In some embodiments, agents in the second pool170 are selected for skills related to agent-customer communications,which can be measured in indicators of customer satisfaction such asfeedback on customer experiences.

FIG. 2 is a flowchart of a call management process 200 for automaticallyrouting an inbound call from a customer to one of a plurality of queues(e.g., two queues) based on predicted value of the inbound telephonecall. Upon identifying the customer, the process retrieves customerdemographic data associated with a customer identifier for theidentified customer. A predictive model, including a logistic regressionmodel operating in conjunction with a tree based model, determines avalue prediction signal for the identified customer. Based on the valueprediction signal determined, the predictive model classifies theidentified customer into one of a first value group and a second valuegroup. In the event the predictive model classifies the identifiedcustomer into the first value group, the call management system routesthe identified customer to a first call queue for connection to one of afirst pool of call center agents who are authorized to present the offerto purchase the product. In the event the predictive model classifiesthe identified customer into the second value group, the call managementsystem routes the identified customer to a second call queue forconnection to one of a second pool of call center agents who are notauthorized to present the offer to purchase the product.

The plurality of steps included in process 200 may be performed by oneor more computing devices or processors in the customer managementsystem of FIG. 1. In an embodiment, the plurality of steps included inprocess 200 may be performed by an inbound queue management system 102of a customer management system 100 of a call center, in operativecommunication with an inbound call receiving system 140 that receives acustomer call of the identified customer.

The call management process 200 is initiated at step 202 in response tothe customer management system 100 receiving an incoming customer callassociated with a customer identifier. Customers may dial into thesystem via a public switched telephone network or via intrasitetelephony. Additionally, inbound calls may be transmitted via theInternet using Web-enabled browsers, personal digital assistants orother network-enabled devices; via mobile cellular telephones (notshown), which may be Web-enabled, or connected via a mobile switchingcenter.

In an embodiment, the customer identifier includes two or more of nameof the identified customer, address of the identified customer, and zipcode of the identified customer. In an embodiment, the customermanagement system obtains a customer identifier for the receivedcustomer call from caller information associated with the inbound calls,such as Automatic Number Identification (“ANI”) and Dialed NumberIdentification Service (“DNIS”) information. In an embodiment, thecustomer management system obtains a customer identifier for thereceived customer call via an automated telegreeter of a Voice ResponseUnit (“VRU”) system. In an embodiment, the customer management systemobtains a customer identifier for the received customer call from athird party directory service.

In an embodiment of step 202, the call management process retrieves thethird party customer demographic data. In an embodiment, the third partycustomer demographic data is associated with the customer identifiercontained in a third party demographic database. In an embodiment, thecall management process builds a simplified data file from the thirdparty demographic data indexed to the customer identifier as input tofurther steps of the process.

A step 204, the call management process determine via a value predictionsignal via a predictive model that includes a logistic regression modeland a tree based model. In various embodiments, the value predictionsignal includes one or more of a first signal representative of alikelihood that the identified customer will accept an offer to purchasea product, a second signal representative of a likelihood that theidentified customer will lapse in payments for a purchased product, anda third signal representative of a likelihood that the identifiedcustomer will accept an offer to purchase the product and will not lapsein payments for the purchased product. In various embodiments, the valueprediction signal is a buy-only signal, a lapse-only signal, abuy-don't-lapse signal, or a combination of these signals.

In various embodiments of step 204, the value prediction signaldetermines a likelihood that the identified customer will lapse inpayments for a purchased product by determining a likelihood that theidentified customer will fail to make a payment for the purchasedproduct during a predetermined time period following purchase of thepurchased product. In an embodiment, the predetermined time period waspreviously determined by modeling lifetime value over varying durationsof the time period following purchase of the purchased product.

In various embodiments of step 204, the regression model employs l₁regularization. In various embodiments, the logistic regression modelemploys l₂ regularization. In various embodiments, the tree based modelis a random forests ensemble learning method for classification.

In an embodiment, the call management process retrieves customerdemographic data from a third party demographic database at step 202,and the logistic regression model of step 204 is trained on a full setof features of the third party demographic database.

At step 206, the call management process classifies the customer call(lead) into either a first value group or a second value group, based onthe value prediction signal determined at step 204. In an embodiment ofstep 206, the first value group includes customers having a first set ofmodeled lifetime values, and the second value group includes customershaving a second set of modeled lifetime values. The modeled lifetimevalues in the first set of modeled lifetime values are higher thanmodeled lifetime values in the second set of modeled lifetime values.

In the event step 206 classifies the identified customer into the firstvalue group, at step 208 the call management process routes theidentified customer to a first call queue for connection to one of afirst pool of call center agents who are authorized to present the offerto purchase the product. In the event step 206 classifies the identifiedcustomer into the second value group, at step 210 the call managementprocess routes the identified customer to a second call queue forconnection to one of a second pool of call center agents who are notauthorized to present the offer to purchase the product.

In various embodiments, the agents to which customers are routed atsteps 208, 210 are associated with a sponsoring organization that sellsor supplies products with the assistance of the call center. In anembodiment, the organization generates sales of one or more productthrough advertisements that give a phone number to potential orprospective customers, and the potential customers call into the callcenter using this phone number. In embodiments of step 206 in which thefirst value group includes leads having modeled lifetime values that arehigher than modeled lifetime values of leads in the second value group,steps 208, 210 prioritize call center resources to pursue sale ofoffered product(s) to the higher-value group of leads.

In certain embodiments of steps 206, 208, and 210, the classification ofleads and routing of leads to the first call queue and second callqueue, can permit adjustments to take into account matching of callcenter resources (agents) to leads at a given time, herein referred toas resource matching adjustments. As seen in the schematic diagram of acustomer queue arrangement 300 at FIG. 3, first call queue 302 consistsof a queue of length M (last queue position 306), to be connected toagents 310 in a first pool of agents who are authorized to present anoffer to purchase the product. Second call queue 304 consists of a queueof length N (last queue position 308), to be connected to agents 312 ina second pool of agents who are not authorized to present an offer topurchase the product.

In various embodiments, resource matching adjustments can includingadjustments to the binary classification model that classifies leadsinto first and second value groups. For example, if there are adisproportionately high number of agents in the first pool 310 comparedto the second pool 312, the threshold for the first value group can bereduced to permit routing of additional leads to the first pool ofagents. In another example, if there are a disproportionately low numberof agents in the first pool 310 compared to the second pool 312, thethreshold for the first value group can be raised to permit routing offewer leads to the first pool of agents.

In various embodiments, resource matching adjustments can includeadjustments to the first queue of leads and second queue of leads. Suchadjustments may take into account the total population of leads awaitingconnection to call center agents, i.e., the leads in both queues 302 and304 at a given time. At certain times there may be an unusualdistribution of leads between the first and second queues, e.g., adisproportionately high number of leads in the first queue (M isunusually high relative to N) or a disproportionately low number ofleads in the first queue (M is unusually low relative to N). To addressthese unusual distributions, one or more customer may be shifted fromthe first queue 302 to the second queue 304, or one or more customer maybe shifted from the second queue 304 to the first queue 302.

Both types of resource matching adjustment, i.e., adjustment of thebinary classification model, and direct adjustment of the call queues,may be combined.

In an embodiment, customer management system 100 utilizes data from bothinternal and external sources in pre-sale predictive modeling of sale ofa financial product (e.g., insurance policy). The data includes internaldata 120 of the call center that tracks historical information aboutleads, customers, and marketing costs of the call center, includinghistorical sales and lapse information. In an embodiment, these internaldatabases use rmm_analytics schema in data warehouse software. In anembodiment, internal databases 120 use rmm_analytics schema in VERTICAto generate a table of enterprise customer data. In another embodiment,internal databases 120 use rmm_analytics schema to generate additionaldata tables, such as a table of historical lead and customer data, and atable of marketing costs data.

In an embodiment, rmm_analytics schema include sales and lapse data forcurrent and/or historical leads of the enterprise, which data is used inbuilding predictive models of the present disclosure. In an illustrativeembodiment, a paid_flag indicates policy payments and a related fieldshows the amount of each payment. In the present disclosure these dataare called payment data. In an illustrative embodiment, either alapse_flag or surrendered_flag indicate that a policy has lapsed. In thepresent disclosure these data are called lapse data. In an embodiment,date fields are used for filtering data by date range. In an embodiment,information about leads, customers, and marketing costs was used tomodel a pre-determined period of time of payments following the sale ofthe product that defines lapse. In an embodiment, for the purpose ofpre-sale predictive modeling of sale of an insurance policy, thismodeling resulted in defining lapse as failure of the customer tomaintain a purchased policy in force for at least 18 months.

In building the predictive models of the present disclosure, modeldatasets may have populations in the hundreds of thousands or millionsof individuals. Model datasets may include training datasets and testingdatasets. Filtering techniques can be applied to eliminate false dataand for de duplicating, reducing the number of records but significantlyimproving quality of model datasets. In an embodiment, date-filtereddata such as payment data and lapse data within an older date range areused for building a training data set, and date-filtered data within amore recent range are used for building a test data set. In anembodiment, predictive machine-learning models of the present disclosureare continually trained using updated payment data, lapse data, andcustomer demographic data.

In an embodiment, in building predictive models, rmm_analytics schema inVERTICA are filtered based on the flow of historical leads through theinbound call center routing process. In an embodiment, the data arefiltered to select only historical leads that were connected to a liveagent; in the present disclosure this flow is sometimes called a “warmtransfer.” Applicant has observed that building predictive models basedon a population limited to warm transfers can improve performance ofmodels for predicting sales and lapse behaviors.

In the embodiment, data used in predictive modeling also include dataretrieved from a customer demographic database 132 to obtain informationabout leads. In an embodiment, customer demographic data includesindividual level data on leads. In various embodiments, as aprerequisite to using data in pre-sale predictive modeling of a giveninbound caller (lead), analytical engine 104 associates the customerdemographic data with a customer identifier for the lead. In anembodiment, customer demographic data used in pre-sale modeling of alead requires an exact match of name and address.

In an embodiment, customer demographic data also includes data usingzip-level features of the system, which provide a coarser representationin building the predictive model. Such zip-level features employvariables in the system that have resolution at the zip-level for eachindividual in the zip code. In an embodiment, zip-level data forindividual income is associated with a median value of income in thesystem for each individual in the zip code. Reasons for using zip-leveldata in predictive modeling include, for example, lack of astatistically significant difference in model performance as a functionof any polymr match score threshold; simplicity of collecting only thename and zip code in the telegreeter process; and privacy considerationsas to individual-level data.

In various embodiments embodiment, in predictive modeling of inboundcallers, inbound queue management system 102 uses a lookup tool (e.g.,polymr) that analyzes customer identifiers of inbound callers in realtime to retrieve customer data, such as customer demographic data,matched to the customer identifiers. In an embodiment, the lookup toolis a lightweight, extensible search engine or API, implemented in thePython object-oriented programming language, https://www.python.org/. Invarious embodiments, the lookup tool performs real time matching of datain the customer demographic database 132 to a customer identifier for agiven lead. In various embodiments, as a preliminary to using data inreal-time predictive modeling of inbound callers, inbound queuemanagement system 102 indexes the data by applying the search engine tocustomer identifiers in customer training data, and stores this index asan internal enterprise database 120.

In an embodiment, inbound queue management system 102 labels each dataelement as continuous (including interval), binary, ordinal, or nominal(categorical). For use in a logistic regression model 114, variablesthat have lookup fields are converted to integers. Following featuretransformation of the variables, the final view outputs each variablewith human-readable names (if known), and a tag at the end of thevariable name. End tags for transformed variable names include:

_binary: either 0 or 1

_ordinal_to_binary: either 0 or 1, where null values are mapped to 0

_flat_binary: mapped from a string field like “01001000” into multiplefields

_ordinal: as an integer, with null values left null

_interval: as an integer, with null values left null

_continuous: as an integer, with null values left null

_nominal: as an integer, with null values mapped to an additionalinteger

By applying the feature transformation rules described above, analyticalengine 104 builds a simplified input data file from data retrieved. Thissimplified input data file facilitates pre-sale predictive modeling witha binary target.

Predictive modeling module 110 builds both a regression model 114 and atree based model 118. In an embodiment, the predictive modeling module110 trains a logistic regression model 114 with l₁ regularization on thefull set of features of the database. Use of logistic regression forclassification problems provides performance advantages over standardlinear regression, because application of the logistic function to theraw model score maps the output precisely from 0→1 while providing asmooth decision boundary. In an embodiment, the logistic regressionmodel with l₁ regularization utilizes LASSO (Least Absolute Shrinkageand Selection Operator), a regression analysis method that performs bothvariable selection and regularization to enhance prediction accuracy andease of interpretation of the resulting statistical model.

l₁ regularization provides the benefit of simplifying the selection offeatures through the model training process by constraining featureswith lower correlation to have 0 weight. The general form for a linearmodel can be indicated as:ŷ(w,x)=w _(o) +w ₁ x ₁ + . . . +w _(p) x _(p)for ŷ to be predicted from data points in the array x by learnedcoefficients w. The l₁ regularization is achieved by adding a term tothe cost function, as follows:

${\min\limits_{w}{\frac{1}{2n_{samples}}{{{Xw} - y}}_{2}^{2}}} + {a{w}_{1}}$with regularization weight α. Applicant observed in training a logisticregression model with l₁ regularization, that run time of trainingincreases rapidly with greater regularization parameters, with bestmodel performance at low values of the regularization parameter α. In anembodiment, the logistic regression model with l₁ regularization setsthe regularization parameter α using cross-validation, withbest-performing values typically around 0.005-0.01.

In another embodiment, regression model employs logistic regression withl₂ regularization, sometimes called ridge regression, according to theformula:

${\min\limits_{w}{\frac{1}{2n_{samples}}{{{Xw} - y}}_{2}^{2}}} + {a{w}_{2}}$

In the l₂ regularization model, as in the l₁ regularization model, theregularization weight α is set by cross validation. In an embodiment, alogistic regression model with l₂ regularization uses a backward featureselection procedure to select an optimal number of features. Thisfeature selection procedure is the RFECV method for recursive featureelimination in Scikit-learn (a software machine-learning library for thePython programming language, available athttps://github.com/scikit-learn/scikit-learn).

In various embodiments, both l₁ and l₂ regularization models fit aregularization hyperparameter using five folds for cross validation andsearching across the seven parameters: [0, 0.001, 0.005, 0.01, 0.1, 0.5,1]. In repeated iterations of model training, this range is restrictedaround previously successful settings.

In an embodiment, the tree based model 118 is a random forests model.Random forests is a class of ensemble methods used for classificationproblems. Random forests models work by fitting an ensemble of decisiontree classifiers on sub samples of the data. Each tree only sees aportion of the data, drawing samples of equal size with replacement.Each tree can use only a limited number of features. By averaging theoutput of classification across the ensemble, the random forests modelcan limit over-fitting that might otherwise occur in a decision treemodel.

In an embodiment, the tree-based model 118 uses the random forests modelin Python's scikit-learn. In an embodiment, the tree-based model 118uses the following parameters in the scikit-learn random forests model:

-   -   Maximum tree depth: 3 or ∞, set with max_depth.    -   Maximum number of features considered when looking for the best        split: 3→6, set with max_features.    -   Minimum number of samples required to split a node of the tree:        2→11, set with min_samples_split.    -   Minimum number of samples to be a leaf node: 1→11, set with        min_samples_leaf.    -   Number of trees in the forest: 100 or 200, set by n_estimators.    -   Whether to sample with replacement for the data seen by each        tree: true or false, set by bootstrap.    -   Function to measure quality of a split: Gini or Entropy        (information gain), set as criterion.

In an embodiment, for each lead, the pre-sale prediction model generatesa value prediction signal indicative of potential value of a salestransaction for that lead. The predictive model can provide varioustypes of value prediction signal including, for example: (a) buy-onlysignal, representative of the likelihood that the customer will acceptthe offer to purchase the product; (b) lapse-only signal representativeof the likelihood that the customer will lapse in payments for thepurchased product; (c) buy-don't-lapse signal representative of thelikelihood that the customer will accept the offer to purchase thefinancial product and will not lapse in payments for the purchasedproduct; as well as predictive models providing combinations of thesesignals.

Predictive models 110 effect a degree of feature selection. In variousembodiments, predictive models 110 identify high importance featuresthat have the most pronounced impact on predicted value. Different typesof model may identify different features as most important. For example,a model based upon a buy-only signal may identify different leadingfeatures than a model based upon a lapse-only signal.

TABLE 1 Features from l₁ buy-don't-lapse model Importance Feature−2.7125 expectant_parent_nominal −0.3126 recent_divorce_nominal_0−0.2634 credit_card_new_issue_nominal_0 −0.1438gender_input_individual_nominal_0 0.1117 socially_influenced_ordinal0.0890 home_length_of_residence_interval −0.0757likely_investors_nominal_0 −0.0667vacation_travel_international_would_enjoy_ordi- nal_to_binary 0.0637total_liquid_investible_assets_fin_ordinal −0.0632 new_mover_nominal_0−0.0518 single_parent_ordinal_to_binary −0.0517vacation_travel_time_share_have_taken_ordi- nal_to_binary −0.0455investments_real_estate_ordinal_to_binary 0.0438investments_stocks_bonds_ordinal_to_binary 0.0429obtain_life_insurance_along_with_loan_mort-gage_installment_payments_ordinal

Table 1 shows the top 15 features from an l₁ buy-don't-lapse model. Themost important features are identified by the highest absolute value ofthe importance coefficient. The most important feature of this target isthe expectant_parent_nominal variable, where a 0 corresponds to notexpectant. Positive and negative signs of the importance coefficientindicate whether an increases, or a decrease, of the feature increaseslikelihood of the target. This data indicates that non-expectant parentsare less likely to buy, and less likely to lapse.

In an embodiment, in building the pre-sale predictive model 110, thecall center evaluates performance of prospective models, such as testmodels, for efficacy in predicting buying behavior and/or lapsebehavior. In an embodiment, prospective models are tested for the areaunder the curve (AUC) of a receiver-operator curve (ROC). FIG. 4 is anexample 400 of an ROC curve 430. The receiver-operating characteristic(ROC) curve plots the true positive rate (Sensitivity) 410 as a functionof the false positive rate (100-Specificity) 420 for different cut-offpoints. Each point on the ROC curve 430 represents asensitivity/specificity pair corresponding to a particular decisionthreshold. An ROC curve with a higher area under the curve (AUC)generally indicates a higher-performing model. The ROC 400 of FIG. 4 wasobtained in testing a logistic regression model with l₁ regularizationon the lapse-only signal, and has an AUC of 0.574, indicating ahigh-performing model.

FIG. 5 is another example of a receiver-operator curve (ROC) 500,obtained by testing a logistic regression model with l₂ regularizationon the buy-only signal trained using all leads. (Sensitivity) 510 as afunction of the false positive rate (100-Specificity) 520 for differentcut-off points. Each point on the ROC curve 530 represents asensitivity/specificity pair corresponding to a particular decisionthreshold. ROC 500 has an area under the curve (AUC) 540 of 0.531.

In an embodiment, prospective predictive models are tested forperformance by measuring lift across deciles. Lift is a measure of thedegree of improvement of a predictive model over analysis without amodel. For a binary classifier model, decile lift is applied to decilesof the target records ranked by predicted probability. FIG. 6 is a graphof lift across deciles of model scores 600 for a logistic regressionmodel with l₁ regularization on the lapse-only signal, trained onzip-level features. Percent of target values 620 across deciles 610 showa significant impact of the model on lapse rate.

In an embodiment, prospective predictive models are tested forperformance by measuring improvements in buying behavior and/orreductions on lapse rate. In various embodiments, these measurements arecarried out with different levels of resource constraint of the callcenter, measured by call center agent resources in view of inbound callvolume. For example, a 70% resource constraint involves agent resourcesat a 70% level of resources in view of call volume relative to fullresources.

In some embodiments, the pre-sale predictive model incorporated alogistic regression model with l₁ regularization, for the lapse-onlytarget. In one embodiment, this model was trained on all customers withindividual-level data. In another embodiment, this model was trained onall customers with zip-level data. At a 70% resource constraint, themodel with individual-level data was tested to provide an 11% reductionin lapse rate, while the model with zip-level data was tested to providean 8% reduction in lapse rate. At a 60% resource constraint, the modelwith individual-level data was tested to provide a 14% reduction inlapse rate, while the model with zip-level data was tested to provide an11% reduction in lapse rate.

While various aspects and embodiments have been disclosed, other aspectsand embodiments are contemplated. The various aspects and embodimentsdisclosed are for purposes of illustration and are not intended to belimiting, with the true scope and spirit being indicated by thefollowing claims.

The foregoing method descriptions and the interface configuration areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe steps in the foregoing embodiments may be performed in any order.Words such as “then,” “next,” etc. are not intended to limit the orderof the steps; these words are simply used to guide the reader throughthe description of the methods. Although process flow diagrams maydescribe the operations as a sequential process, many of the operationscan be performed in parallel or concurrently. In addition, the order ofthe operations may be re-arranged. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination may correspond to a return ofthe function to the calling function or the main function.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedhere may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description here.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed here may be embodied in a processor-executable software modulewhich may reside on a computer-readable or processor-readable storagemedium. A non-transitory computer-readable or processor-readable mediaincludes both computer storage media and tangible storage media thatfacilitate transfer of a computer program from one place to another. Anon-transitory processor-readable storage media may be any availablemedia that may be accessed by a computer. By way of example, and notlimitation, such non-transitory processor-readable media may compriseRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other tangible storagemedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computeror processor. Disk and disc, as used here, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or instructions on anon-transitory processor-readable medium and/or computer-readablemedium, which may be incorporated into a computer program product.

What is claimed is:
 1. A processor-based method, comprising: uponestablishing a call session with an identified customer at an inboundcall receiving device: retrieving, by a processor, customer demographicdata for the identified customer; executing, by the processor, apredictive machine-learning model configured to determine a valueprediction signal by inputting customer demographic data, wherein thevalue prediction signal is representative of a likelihood that theidentified customer will accept an offer to purchase a product, thepredictive machine-learning model classifying the identified customerinto a first value group or into a second value group, wherein a modeledvalue of sale of the product of the first value group is higher than amodeled value of the sale of the product of the second value group;directing, by the processor, the inbound call receiving device: to routethe identified customer to a first call queue for connection to one of afirst pool of call center agents in the event the processor classifiesthe identified customer into the first value group; and to route theidentified customer to a second call queue for connection to one of asecond pool of call center agents in the event the processor classifiesthe identified customer into the second value group.
 2. The processorbased method according to claim 1, wherein the first value groupcomprises customers having a first set of modeled lifetime values ofsale of the product, and the second value group comprises customershaving a second set of modeled lifetime values of sale of the product,wherein modeled lifetime values in the first set of modeled lifetimevalues are higher than modeled lifetime values in the second set ofmodeled lifetime values.
 3. The processor based method according toclaim 1, wherein the predictive machine-learning model is furtherconfigured to determine the value prediction signal by inputting paymentdata and marketing costs data.
 4. The processor based method accordingto claim 3, wherein the predictive machine-learning model is continuallytrained using updated customer demographic data, updated payment data,and updated marketing costs data.
 5. The processor based methodaccording to claim 3, wherein the predictive machine-learning model isfurther configured to determine the value prediction signal by inputtinglapse data.
 6. The processor based method according to claim 1, whereinthe first model comprises a logistic regression model.
 7. The processorbased method according to claim 6, wherein the logistic regression modelcomprises one of a logistic regression model with l₁ regularization anda logistic regression model with l₂ regularization.
 8. The processorbased method according to claim 1, wherein the second model comprises atree based model.
 9. The processor based method according to claim 8,wherein the tree based model comprises a random forests ensemblelearning method for classification.
 10. The processor based methodaccording to claim 1, wherein the first pool of call center agentscomprises agents who are authorized to present the offer to purchase theproduct, and the second pool of call center agents comprises agents whoare not authorized to present the offer to purchase the product.
 11. Aprocessor based method for managing customer calls within a call center,comprising: retrieving, by a processor, customer demographic dataassociated with a customer identifier for an identified customer in acustomer call, via a lookup tool executing on the processor to performreal time matching of customer demographic data to the customeridentifier for the identified customer in the customer call;determining, by a predictive model executing on the processor, a valueprediction signal representative of a likelihood that the identifiedcustomer will accept an offer to purchase a product, wherein thepredictive model comprises a first model operating in conjunction with asecond model; classifying, by the predictive model executing on theprocessor based on the value prediction signal determined by thepredictive model, the identified customer into one of a first valuegroup and a second value group, wherein a modeled value of sale of theproduct of the first value group is higher than a modeled value of thesale of the product of the second value group; and in the event theclassifying step classifies the identified customer into the first valuegroup, routing, by the processor, the identified customer to a firstcall queue for connection to one of a first pool of call center agents;in the event the classifying step classifies the identified customerinto the second value group, routing, by the processor, the identifiedcustomer to a second call queue for connection to one of a second poolof call center agents.
 12. The processor based method according to claim11, wherein the first value group comprises customers having a first setof modeled lifetime values of sale of the product, and the second valuegroup comprises customers having a second set of modeled lifetime valuesof sale of the product, wherein modeled lifetime values in the first setof modeled lifetime values are higher than modeled lifetime values inthe second set of modeled lifetime values.
 13. The processor basedmethod according to claim 11, wherein the first model comprises alogistic regression model and the second model comprises a tree basedmodel.
 14. The processor based method according to claim 13, wherein thelogistic regression model employs one of l₁ regularization and l₂regularization.
 15. The processor based method according to claim 13,wherein the tree based model comprises a random forests ensemblelearning method for classification.
 16. The processor based methodaccording to claim 13, wherein the predictive machine-learning model isconfigured to determine the value prediction signal by inputtingcustomer demographic data, payment data, and marketing costs data intothe logistic regression model operating in conjunction with the treebased model.
 17. The processor based method according to claim 11,wherein the first pool of call center agents comprises agents who areauthorized to present the offer to purchase the product, and the secondpool of call center agents comprises agents who are not authorized topresent the offer to purchase the product.
 18. A system for managingcustomer calls within a call center, comprising: an inbound telephonecall receiving device for receiving a customer call to the call center;non-transitory machine-readable memory that stores financial informationof the call center for historical product sales; a predictive modelingmodule that stores a predictive model of customer value, wherein thepredictive model comprises a first model operating in conjunction with asecond model; and a processor, configured to execute an inbound queuemanagement module, wherein the processor in communication with thenon-transitory machine-readable memory and the predictive models moduleexecutes a set of instructions instructing the processor to: retrievecustomer demographic data associated with a customer identifier for anidentified customer in the customer call received by the inboundtelephone call receiving device; retrieve from the non-transitorymachine readable memory the financial information of the call center forhistorical product sales; determine a value prediction signal for theidentified customer via analysis by the predictive model of the customerdemographic data associated with the customer identifier for theidentified customer and the financial information of the call center forhistorical product sales; wherein the value prediction signal isrepresentative of a likelihood that the identified customer will acceptan offer to purchase a product; classify the identified customer intoone of a first value group and a second value group based on the valueprediction signal, wherein a modeled value of sale of the product of thefirst value group is higher than a modeled value of the sale of theproduct of the second value group; and direct the inbound telephone callreceiving device: in the event the inbound queue management moduleclassifies the identified customer into the first value group, to routethe identified customer to a first call queue for connection to one of afirst pool of call center agents; in the event the inbound queuemanagement module classifies the identified customer into the secondvalue group, to route the identified customer to a second call queue forconnection to one of a second pool of call center agents.
 19. The systemaccording to claim 18, wherein the first model is a logistic regressionmodel and the second model is a tree based model.
 20. The systemaccording to claim 19, wherein the logistic regression model comprisesone of a logistic regression model with l₁ regularization and a logisticregression model with l₂ regularization, and the tree based model is arandom forests ensemble learning method for classification.